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While deep Embedding Learning approaches have witnessed widespread success inmultiple computer vision tasks, the state-of-the-art methods for representingnatural images need not necessarily perform well on images from other domains,such as paintings, cartoons, and sketch. This is because of the huge shift inthe distribution of data from across these domains, as compared to naturalimages. Domains like sketch often contain sparse informative pixels. However,recognizing objects in such domains is crucial, given multiple relevantapplications leveraging such data, for instance, sketch to image retrieval.Thus, achieving an Embedding Learning model that could perform well acrossmultiple domains is not only challenging, but plays a pivotal role in computervision. To this end, in this paper, we propose a novel Embedding Learningapproach with the goal of generalizing across different domains. Duringtraining, given a query image from a domain, we employ gated fusion andattention to generate a positive example, which carries a broad notion of thesemantics of the query object category (from across multiple domains). Byvirtue of Contrastive Learning, we pull the embeddings of the query andpositive, in order to learn a representation which is robust across domains. Atthe same time, to teach the model to be discriminative against examples fromdifferent semantic categories (across domains), we also maintain a pool ofnegative embeddings (from different categories). We show the prowess of ourmethod using the DomainBed framework, on the popular PACS (Photo, Art painting,Cartoon, and Sketch) dataset.

Fuse and Attend: Generalized Embedding Learning for Art and Sketches